clustering

One of the main uses of text mining is to help discover hidden knowledge among various texts that business people seek to acquire. The current research aims to discover knowledge from the textual analysis of articles related to the field of health tourism marketing.

Design/methodology/approach: 

In the first step, a total of 456 articles between 2010 and 2024 were selected using the term "health tourism marketing" in databases and valid search engines. Through reviewing the title, abstract, and main text of the articles and in different stages of screening the articles, the researchers finally selected 42 articles related to the topic of health tourism marketing to perform the text mining process. In the second step, after pre-processing the data, using non-hierarchical cluster analysis, the number of 3 optimal clusters was obtained according to the Davies-Bouldin index, and a keyword was drawn for each cluster. Then, using the supporting indicators, the confidence and attractiveness of the associative rules of each cluster were extracted and analyzed. In the third step, using the results of the first and second steps, a fishbone diagram of the research was drawn and a summary of the results was presented in the form of a diagram.

Applying cluster analysis led to the placement of articles in three clusters based on the Davis-Bouldin index, in the zero, first, and second clusters respectively, the words service, patient and tourism occupied the centrality of the cluster and in order to provide a better view of the keywords of each cluster was drawn. The application of associative rules led to the identification of if-then rules separately for each cluster, from among thousands of extracted rules, 15 rules were presented separately for each cluster based on attractiveness, support, confidence, and experts' opinions.

Summarizing the results and designing the fishbone diagram showed that in order to exploit the benefits of health tourism, it is necessary to improve and optimally use natural, hard, soft and marketing infrastructures.

Originality/value:

 Conducting many researches in the field of health tourism shows the importance and micro-macro benefits of this field, so understanding and explaining the researches of this field through text mining can be the beginning of a way to exploit the knowledge accumulated in it.

Many research have been made about of polar vortex (P.V) and its effects on precipitation in the world and in the Iran. Most of them emphasize the direct effect of the polar vortex on winter precipitation. Lashkari (1375) investigated the synoptic patterns of heavy rainfalls in the southwest of the Iran. The intensity, weakness and fluctuations of the subtropical high-pressure system restrain the climate of the mid-latitudes, including the Middle East and Iran. According to the related researches, in the cold season, when subtropical high pressure belt retreats the polar vortex goes forward and causing the transfer of energy and momentum and creates meridian waves. At the beginning of the warm period with the weakening and retreat of the polar vortex, the subtropical high pressure goes forward to north and penetrates to middle East and Iran and arid season is started.

 StatisticsThis paper has focused on the effects of polar vortex on the extreme and widespread precipitation in the Midwest of the Iran for a 30 years period (1986-2015). In the statistical section, after collecting the rainfall data above 1 mm and considering the minimum rainfall of 10 mm , three percentiles of 90, 95 and 98 were selected and based on this, 5 rainfall categories were introduced. By using the Mann-Kendall test, the average of heavy and super heavy rains was obtained, which showed that 50% of the stations had a decreasing and negative trend and 50% of them had an increasing and positive trend. The classification of heavy rains by the Mann-Kendall test also shows that among 18 stations 13 stations (72% of the data) had a positive and upward trend. In addition none of the stations had significant trends in rainfall. In the next stage, the correlation of the polar vortex passing curve from the region (5500 Gpm) with extreme precipitations has been investigated.The geopotential values of this curve with the amounts of rainfall was tested statistically, and the average rainfall with the transition curve was found to be significant at the level of 0.05 (-0.120), which indicates the decrease of rainfall with the north displacement of the polar vortex curve. On the other hand, in the extreme rainfalls condition in the study area, the passing polar vortex curve over the study region at the 500 Hpa level has suffered a southward drift and geopotential height drop in historic period. synopticSynoptic patterns of heavy rainfalls at 850 and 500 Hpa levels are classified by using cluster analysis. In clustering level 850 after selection the representative days of each pattern, the synoptic maps in level of 850 Hpa and vorticity, relative humidity, wind vectors in level of 700 Hpa, and finally the maps of jet stream in 250 Hpa level were drawn. In these maps northern, central and southern representative curves of polar vortex, the location of jet stream, isopleths of vorticity and their relationship were drawn. Then, by classifying the data of representative days at the 500 Hpa level for both heavy and super heavy rainfalls, several patterns were obtained and in each map the wind above 30 m/s and the representative curves of vorticity are marked. It should be noted that the created maps are the result of the integration of manual and visual methods.

The results of this research showed that in most of the stations there is an inverse relationship between the criterion rainfalls and polar vortex curves. In fact, with the northward and southward movement of the central polar vortex curve, there are decrease and an increase in rainfall in the study area. According to other studies, there is a direct relationship between heavy and super heavy rainfalls with the curves of polar vortex. In fact by the southward movement of polar vortex curves the rainfall in the region has increased. Among the three curves that are considered for the polar vortex, the main one and the most impact is related to the central or 5500 Gpm curve. The correlation coefficient of the geopotential height of the transition curve and the amount of precipitation of each category is -0.12, which is significant at the 5% level. The mentioned number indicates the decrease of precipitation in the area that is related to the changes in the curve of the P.V at 500 Hpa level.This precipitation's change can be the effect of climate change which has moved the area of subtropical high pressure to higher latitudes by about 4-5 degrees. These conditions cause the northward displacement of the polar vortex curves and the curve passing through the study region at the level of 500 Hpa and it has caused a decrease in rainfall (non-significant) during the study historical period. In the synoptic section, heavy and super heavy rains occur in relation to the synoptic weather patterns of 850, 700 and 500 Hpa levels were investigated. In the most of the stations and patterns that were obtained, precipitation indicates the presence of low pressure systems from the Arabian, Red and Mediterranean seas as premier cause of rainfall in the study region. By checking the position of the jet stream at the upper level and the moisture advection at the 700 Hpa level, it can be recognized that the most moisture that enters to the west of Iran comes from the Arabian and Red seas. According to these investigations, there is a direct link between temporal relationship of these rainfalls and the activity period of the polar vortex. These systems have entered to the country through displacement of polar vortex and have caused heavy and super heavy rains in the Midwest of Iran.

The occurrence of heavy rains is related to the synoptic patterns of the earth's surface and the upper levels of the atmosphere. The main goal of this research is to find the synoptic-thermodynamic pattern of heavy rainfall in selected stations of Kermanshah province. For this purpose, daily rainfall data and maps of sea level and height geopotential of 500 hPa were obtained for the statistical period (2010-2019) and were processed using principal component analysis (PCA) to identify the components. CAPE, LI, KI, TTI and SI instability indices were used for thermodynamic analysis. The results of the factor analysis showed that three factors in total explain and justify 80.5% of the variance of productive patterns. Further, four patterns were obtained by clustering using Ward's integration method and Euclidean distance. These patterns include the eastern Mediterranean and Iraq descent, the low-altitude Mediterranean cut bandal, the deep Russian descent and the eastern Mediterranean and Iraq quasi-omega bandal. The results showed that in the days of heavy rain, the dominant pattern of low pressure was located over the eastern Mediterranean Sea and Iraq and the location of the southwestern part of the omega-shaped bandal and the eastern Mediterranean trough in the region, which had the highest frequency of heavy rain with 22 days out of 38 days. The supply of moisture these days from the Mediterranean and Red seas, the presence of relative humidity close to saturated humidity and negative omega has led to an increase in humidity in the air, the rise of moist air, strengthening convection, creating an unstable atmosphere and heavy rains in the region. The results of thermodynamic analysis showed that the instability indices related to the second and third patterns were not very strong, but in the first and fourth patterns, the indices revealed severe instability and high potential.

This research has been conducted with the aim of identifying heat waves in Ardabil province and analyzing their synoptic patterns using a circular method. For this purpose, the daily average maximum temperature data of Ardabil, Parsabad and Meshkinshahr synoptic stations were used in the statistical period of 1980-2020. The 95th percentile index was determined as a criterion for identifying a hot day, and its duration of at least 3 days was considered as the basis for defining a heat wave. Examining the trend of SLP, HGT and Tmax of these waves showed that on the hottest days of heat waves, the trend of SLP is downward and the trend of HGT and Tmax is upward. Clustering of heat wave generation patterns based on reanalysis data of sea level pressure and geopotential height of 500 hectopascals using cluster analysis method and Ward's clustering method led to the identification of 5 clusters which were presented in two main groups. In the first pattern, low pressure on the ground surface and high pressure in the middle level of the atmosphere, and in the second pattern, the location of the studied area in the west of the pseudo-Omega blocking of western Russia at the level of 500 hectopascals were identified as the synoptic factors of creating heat waves in Ardabil province. The common feature of both patterns is the high thickness of the atmosphere over the region on the days of this event.

The phenomenon of precipitation is the final product of several complex atmospheric processes that are different in terms of time and place, and it can be considered one of the climatic characteristics of each region. The diversity of origin, temporal-spatial changes of rainfall systems, ground conditions such as altitude and topography, and distance and proximity to moisture sources cause precipitation behaviors, including continuity, to have temporal-spatial changes. Heavy and continuous rains are significantly effective in supplying water, feeding the underground water table, and storing it in the soil. It can also cause landslides, destruction of vegetation of forests and pastures, severe soil erosion, destruction of water structures, and waste of high volume of surface water resources in arid and semi-arid areas; therefore, it has always interested climate and meteorology researchers. In Iran, heavy and moderate rainfalls have been investigated from different perspectives. Some researchers have investigated heavy rains leading to floods in different regions of the country, and others have zoned rains lasting several days using the clustering method. Despite the various analyses conducted by the researchers, due to the difference in the selection of rainfall amounts and the different behavior of synoptic systems, the factors affecting rainfall lasting several days remain in the veil of ambiguity. This research aims to identify the synoptic patterns of rainfall lasting more than 4 days and to investigate the horizontal flux of specific moisture so that by identifying the behavior of this variable and its related fluctuations, accurate and comprehensive planning can be carried out in the direction of managing the country's water resources. What distinguishes this research from other studies is the method of estimating the limited rainfall of the study stations and the selection of the statistical period leading to 2022.

Methodology (Materials and Methods):

This research used two series of data; the first series was the daily rainfall data of 120 synoptic stations of the country from its establishment until 2022, which was taken from the National Meteorological Organization. First, the total four-day rainfall of weather stations from October to March was calculated. Then, based on the Andersen-Darling index, the best probability distribution function was fitted to the obtained data, and precipitation values above 99% of the fitted curve were obtained, among which the heaviest precipitation was selected. Five distinct clusters were identified using the hierarchical clustering technique based on the height of the middle level of the atmosphere. From each cluster, a precipitation system was chosen for synoptic analysis based on three criteria - intensity, continuity, and comprehensiveness of precipitation.The second series of data used in this research, which was used for the case study of synoptic systems, was taken from the NCEP/NCAR archive. These data in a regular grid are limited to 0 to 80 degrees east longitude and 10 to 60 degrees north latitude with a grid step of two and a half degrees, including an area where synoptic waves can be detected. From this series of data, we can refer to the average sea level pressure, altitude, temperature, and relative humidity of the pressure level of 850 hPa, the wind field and specific humidity of the pressure levels of 925 and 700 hPa, the height and the vertical component of the wind level of 500 hPa and the wind field of the level of 200 hPa.

Five specific patterns were obtained in the hierarchical clustering of the middle level's height. In the four obtained patterns, mid-level troughs with different depths are located in different geographical positions, and in the other pattern, a cut low pressure with a cold core was revealed in the north of the Black Sea. From examining the uncovered troughs, it was seen that in two cases, in the eastern part of the trough, there was a strong pile of height, which prevented the eastward movement of the trough and made it last. In these cases, the stack becomes more profound as the ship's depth increases and the ship's movement becomes slower. In two cases, short and low-amplitude waves caused by the rotating movement of the ship periodically passed through the study area and caused the instability of the atmosphere. In the case of a low-pressure cut, a deep ridge has caused the system to last and continue to rain. Since the water vapor is more in the lower levels above the water sources, the upward movement caused by the eastern circulation of pressure systems has caused the water vapor to be transferred into it and increased the amount of precipitation. Therefore, the persistence and transfer of water vapor have caused the continuation of precipitation in the study area.

Investigations showed that the synoptic systems of mid-latitudes, when passing over the warm waters of the Mediterranean Sea, caused an upward movement due to the gyre circulation and transferred a large amount of moisture on the sea to higher altitudes. In addition, the eastern winds of the high-pressure southern part over the Oman Sea have transferred a large amount of special moisture to the south of the Red Sea, which has been transferred to the south, southwest, and western regions of Iran with the southern winds of the eastern part of the Red Sea pressure trough. As a result of the upward movement, these values have participated in the precipitation process and increased the amount of precipitation and the continuity of rain in the western half of Iran.

Tourism clusters refer to a collection of tourism business units concentrated in a geographical area and have specialized cooperative relationships Many countries use clustering strategy to overcome existing problems and tourism businesses. The aim of this study was to cluster villages with tourism potential in Tabas city. The research method is descriptive-analytical research and its dominant approach is survey. Library and field methods have been used to collect data. The statistical population of the study consisted of 36 villages with tourism potential of Tabas city in South Khorasan. In order to cluster the villages, the opinions of 30 experts (cultural heritage, bakhsh and governor's staff, etc.) were investigated. Also, ARRAS model was used to rank the villages and GIS software was used to show the results and clustering of villages. The results show that the villages of Hodar, Jowkhah, Izmighan, Crete, Dastgaran, Nayband, Esfandiar, Zenoghan, Robat Khan, Halvan, Dehno and Arababad have strong potential to attract tourism. And the high-quality villages, Khoroulia, Pikweh, Malvand, Hassan Abad, Kordabad, Nosratabad, Pirhajat, Chirok, Sarand, Mawader have less potential to attract tourists.

Clustering is a vital technique for revealing structures and discerning groupings within extensive datasets, particularly in spatial data analysis, where the primary objective is to segregate data into clusters with shared characteristics. Artificial neural networks are established tools for clustering large and multidimensional datasets. This research focuses on clustering census block data, encompassing 21 socio-economic variables and access to services relevant to sustainable urban development. The study employs the Neural Gas (NG) network without spatial parameters. Then, it introduces the geographic coordinates of census blocks as spatial parameters, comparing the outcomes of the two approaches (NG & CNG). The NG algorithm, a prevalent choice for clustering high-dimensional data, and its spatially enhanced version, the Contextual Neural Gas (CNG) algorithm, were employed in clustering Isfahan city's census blocks. Results indicated a notable distinction in the clusters derived from the implementation of the NG and CNG algorithms. Clustering with the NG algorithm yielded heterogeneous clusters, whereas the CNG algorithm produced homogeneous clusters benefiting from spatial parameters. Evaluation of clustering quality, performed by calculating the average Silhouette coefficient for census blocks, showed the superior performance of the CNG algorithm, attaining a silhouette coefficient of 0.29 compared to the NG algorithm's -0.02. This research affirmed the positive impact of spatial parameters on creating homogeneous clusters within the urban environment. Leveraging the CNG algorithm and extracting homogenous areas based on sustainable development variables contributed to streamlined urban planning and management. The clustering of census blocks using variables related to sustainable urban development and a location-based approach using the CNG algorithm is one of the innovations of this research.

The Transportation-Based Development (TOD) approach is a type of development of living and working spaces that is defined based on the distance from the public transportation station and the main users in this development are pedestrians who are at a distance of 400 to 800 meters or about 5 to 10 minutes' walk provides them with the ability to walk comfortably and safely. In TODI's approach, there is a range of use intensity and density and height of buildings; This means that in the areas closer to the station, it provides more services to pedestrians and less services to private cars. Near the station, more compaction and mixing of uses is required, and this intensity decreases as it moves away from the station. The TOD approach is a type of development with mixed-use, compact, and high density, defined by easy access to public transportation. In Iran, the definition of TOD has been mistakenly reduced to a purely stationary place around the metro, and TOD projects have failed to meet the demands and expectations of the city. In this study, to answer the two main questions, i.e., which are the most appropriate urban metro stations for TOD implementation in Isfahan, and what is the right approach to achieve them, TOD indicators were developed. Additionally, by examining Isfahan’s capacities, areas that are suitable for the TOD approach were identified. By proposing the ranking algorithm and testing it, TOD potential areas in Isfahan metro have been prioritized. Darvaze- Dolat, Atiq Square and Azadi Square metro stations scored the highest points for TOD realization. Moreover, through the clustering method in Expert Choice software, a planning framework for TOD potential areas has been proposed.

Today, housing has found a concept beyond a shelter and plays an important role in how the physical quality of cities. In fact, one of the most important ways to know the status of housing in the housing planning process is to use housing indicators. These indicators, which indicate the quantitative and qualitative status of housing in any period of time, can be considered as a suitable guide to improve housing planning for the future. Therefore, the present study was conducted with the aim of leveling the neighborhoods of Bojnourd city based on housing indicators. The research is descriptive-analytical in nature and applied according to its purpose. The method of collecting information in the form of documents is library. In this study, 11 indicators related to housing quality were used to rank neighborhoods, and the Waspas model was used to rank neighborhoods. The statistical population of the study includes 42 neighborhoods of Bojnourd. Based on the results, it was found that half of the neighborhoods (22 neighborhoods) of Bojnourd  are in a favorable and completely desirable housing condition, in contrast to 28% of the city neighborhoods are in a relatively favorable condition that the number of population at this level is equal to It is 31% of the total population of the city. Finally, there is the unfavorable housing situation, which includes 8 neighborhoods of the city. The total population of these neighborhoods is equal to 17% of the city's population, which is located in an area of ​​973 hectares.

The purpose of this article is to analyze the frequency and severity of the one to six days of rainfall in Iran. The trend of frequency changes and severity of each course was identified using my-candle test and the slope estimator during the 1968-1988 period. Then, using the main component analysis method and cluster analysis method, the entire stations were categorized in five clusters (abundance) and four (intensity) based on the annual changes of frequency indicators and intensity of precipitation. Cluster 1 and 2 stations represent the frequency of precipitation periods with a severe or without trend. The two clusters were mostly established in the southern half of Iran. Cluster 4 and 5 stations represent the frequency of precipitation periods with a positive (mild) trend, mainly in the northern part of the country. Cluster 3 stations represent the frequency of precipitation periods with decreased (mild) trends, which are mostly focused on west and southwestern Iran. The clustering results of the stations based on the intensity index of precipitation periods, contrary to many results; do not show a specific pattern. But in the cluster, there has been a severe decrease in the last half century. The stations of this cluster are mostly concentrated in the northern parts of the country. Other clusters are scattered in almost all parts of the country. Accordingly, it can be concluded that the frequency of precipitation periods in the northern latitudes of incremental processes (average or weak) and the severity of precipitation periods in these latitudes (north of the country) had severe declining trends.

Clustering is an instrument that divides existing data into different groups. Generally, the number of clusters is determined based on the least changes within the group and the most changes outside the group. The study area is country of Iran. Coordinates of longitude, latitude, altitude, average temperature, relative humidity and total monthly rainfall of 420 synoptic stations from its establishment until 2018 have been used in this study. After reviewing, screening and repairing the data, only 375 stations remained to continue the research. Due to the length of the statistical period is an important factor influencing clustering, the stations are statistically divided into three periods: less than 5 years with 42 stations; 1-6 years with 33 stations and more than 10 years with 300 stations, were classified. Seven methods of hierarchical clustering (3 subsets), separation (2 subsets) and ward (2 subsets) have been used in this study. Cophenetic correlation coefficient, Silhouette width test are two indicators of clustering and selection. The coding was performed in R statistical software. Based on the Cophenetic and Silhouette coefficient indices, the best number and method of clustering for 1-5-year data are 4 clusters with the middle axis separation method, for the data of 6-10 years are 5 clusters with the mean-centered hierarchical method and for stations with a statistical period of more than 10 years are 4 clusters with the separation average axis method. The zoning of the clusters is plotted on the geographical map of Iran using ARCGIS software for all three categories. Keywords: Clustering, Geographical coordinates, Synoptic, Iran.Clustering is an instrument that divides existing data into different groups. Generally, the number of clusters is determined based on the least changes within the group and the most changes outside the group. The study area is country of Iran. Coordinates of longitude, latitude, altitude, average temperature, relative humidity and total monthly rainfall of 420 synoptic stations from its establishment until 2018 have been used in this study. After reviewing, screening and repairing the data, only 375 stations remained to continue the research. Due to the length of the statistical period is an important factor influencing clustering, the stations are statistically divided into three periods: less than 5 years with 42 stations; 1-6 years with 33 stations and more than 10 years with 300 stations, were classified. Seven methods of hierarchical clustering (3 subsets), separation (2 subsets) and ward (2 subsets) have been used in this study. Cophenetic correlation coefficient, Silhouette width test are two indicators of clustering and selection. The coding was performed in R statistical software. Based on the Cophenetic and Silhouette coefficient indices, the best number and method of clustering for 1-5-year data are 4 clusters with the middle axis separation method, for the data of 6-10 years are 5 clusters with the mean-centered hierarchical method and for stations with a statistical period of more than 10 years are 4 clusters with the separation average axis method. The zoning of the clusters is plotted on the geographical map of Iran using ARCGIS software for all three categories. Keywords: Clustering, Geographical coordinates, Synoptic, Iran.Clustering is an instrument that divides existing data into different groups. Generally, the number of clusters is determined based on the least changes within the group and the most changes outside the group. The study area is country of Iran. Coordinates of longitude, latitude, altitude, average temperature, relative humidity and total monthly rainfall of 420 synoptic stations from its establishment until 2018 have been used in this study. After reviewing, screening and repairing the data, only 375 stations remained to continue the research. Due to the length of the statistical period is an important factor influencing clustering, the stations are statistically divided into three periods: less than 5 years with 42 stations; 1-6 years with 33 stations and more than 10 years with 300 stations, were classified. Seven methods of hierarchical clustering (3 subsets), separation (2 subsets) and ward (2 subsets) have been used in this study. Cophenetic correlation coefficient, Silhouette width test are two indicators of clustering and selection. The coding was performed in R statistical software. Based on the Cophenetic and Silhouette coefficient indices, the best number and method of clustering for 1-5-year data are 4 clusters with the middle axis separation method, for the data of 6-10 years are 5 clusters with the mean-centered hierarchical method and for stations with a statistical period of more than 10 years are 4 clusters with the separation average axis method. The zoning of the clusters is plotted on the geographical map of Iran using ARCGIS software for all three categories. Keywords: Clustering, Geographical coordinates, Synoptic, Iran.

The increasing volume and dimensions of spatial data have made self-organizing neural networks a prominent tool for analyzing large and multi-dimensional datasets. Clustering, an approach for extracting knowledge from big data, aims to group similar data into clusters. This research focused on clustering socio-economic data of census blocks associated with urban sustainable development using self-organizing neural networks with and without spatial parameters referred to as SOM and Geo-SOM, respectively. Both algorithms employ the same clustering process but differ in the inclusion of spatial parameters, specifically the geographic coordinates of block centroids, in the Geo-SOM algorithm. The SOM and Geo-SOM algorithms were trained and applied to cluster the data. The resulting clusters exhibited distinct dissimilarities, demonstrating that clustering census block data solely based on non-spatial attributes leads to heterogeneous and incongruent clusters, whereas incorporating spatial parameters yields homogeneous and congruent clusters. Evaluation of the results using Silhouette coefficient indicated that Geo-SOM outperformed SOM in clustering the data with average Silhouette coefficients of -0.02 and 0.27 for SOM and Geo-SOM, respectively. Comparison of the outcomes highlighted the positive impact of incorporating spatial parameters on clustering socio-economic data.

Landslide, as an important natural hazards, causes damage to infrastructure and leads to economic, social and human losses. In this regard, determining the critical zones of landslides occurrence can be used in planning for damage reduction. The aim of the current research is analyzing and identifying landslide hot spots using Getis-Ord algorithm in Gharnaveh watershed, Golestan province. Therefore, the frequency and characteristics of landslides has been analyzed in different classes of slope, aspect, soil and land uses. The area, length, width, depth and height of the precipice of landslide features have been considered as the basis in hot spot analysis. The results showed that the landslide hot spots are located in the eastern part of the study area, which includes high altitude classes, rangelands and moderate slopes. Results showed that the rangeland and forest land uses, loess soils and 50-75% slope class and the northern aspect had the highest number of landslides. Also, the non-significant landslide points using the Getis-Ord method and considering landslide area criterion located in the middle and downstream of Gharnaveh watershed. Determining the landslide hotspots and affecting factors through the spatial analysis in GIS provides the defining thresholds in the landslide’s occurrence. The analysis of the landslide hotspots can be a basis for spatial planning, and risk reduction. The implemented approach can be used in the evaluation of the spatial autocorrelation of natural hazards, and in combination with the areas prone to multiple environmental hazards can predict the risk and severity of damages in the future.

Tourism clusters refer to a set of tourism business units which are concentrated in a geographical area and have specialized cooperation relations. Many countries are trying to overcome the existing issues and challenges in tourism businesses by using the clustering strategy. The purpose of the research is to explain the spatiality of tourism clusters in villages with tourism potential in Tabas region. The descriptive-analytical research method is used and its dominant approach is based on survey; meanwhile, literature review and field study methodologies have been used to collect data. The statistical population includes 36 villages with tourism potential in Tabas region in South Khorasan province. The modified Cochran formula was used to determine the sample size in this study. Based on this, out of 3675 rural households in Tabas city, 348 households were selected as samples. In order to cluster the villages, the opinions of 30 experts (employees of cultural heritage, prefectural and governorate, etc.) were included. Also, Aras model was used to rank the studied villages and ARc GIS software was used to calculate the records and cluster the villages. The results of the research indicate a positive and significant relationship between the arrival of tourists and the economic, social and environmental dimensions. The results of the t-test with a confidence level of 95%, the level of most of the indicators among the villagers is higher than the average level. Furthermore, by considering the results of Aras and Gutman's technique, it can be seen that the villages of Hoder, Jokhah, Azmeighan, Kirit, Dastgaran, Naiband, Esfandiar, Zenughan, Rabat Khan, Helwan, Dehnu and Arababad have a strong potential to attract tourism. By contrast, the creation of tourism clusters, and the villages of Khorualiya, Marghoub, Pikoh, Melvand, Hasan Abad, Kordabad, Nusrat Abad, Pirhajat, Chirok, Serand, Mawder have less potential to attract tourists.

National competitiveness as an important economic goal in the context of globalization has been considered by many policymakers around the world. In the Iran, national competitiveness has been considered by officials and the government of men, so that achieving Iran's top economic, scientific, and technological position among the countries, especially among the countries of Southwest Asia, is one of the most important goals in the 20-Year Vision Document. Therefore, providing a framework for identifying and prioritizing factors that help improve Iran's competitiveness can be effective in achieving this goal.

Methodolog:

This study intends to provide a new framework based on the Global Competitiveness Index to prioritize the competitiveness pillars to increase the national competitiveness of Iran and determine the benchmark countries. This framework is implemented in three stages. The countries of the world are first clustered on the basis of competitive pillars using the fuzzy c-means clustering algorithm to distinguish the countries that are more similar in terms of national competitiveness. The competitiveness pillars in each cluster are then weighted using the CCSD weighting method. The weights obtained indicate the importance and priority of competitive factors. Finally, the WASPAS method is used to rank the countries in each cluster. These rankings are used to identify benchmark countries in each cluster.

Based on the results of this study, the countries of the world were first classified into three clusters. Clusters 1, 2 and 3 represent the developing, least developed, and the developed countries, respectively. After clustering the countries, competitive pillars were weighed using the CCSD weighting method. Accordingly, the labor market in cluster 1, the product market in cluster 2, and macroeconomic stability in cluster 3 had the highest weights. The higher the weight of a pillar, the higher its importance and priority in terms of competitiveness. For the Iran, labor market, macroeconomic stability and infrastructures were the most important competitiveness factors, respectively. Finally, the countries of each cluster were ranked using the WASPAS method. Latvia, Thailand, and Cyprus in cluster 1, Namibia, Kenya, and Macedonia in cluster 2, and Singapore, United States and Switzerland were top three ranked countries. Iran, with a rank of 58, is among the last countries in cluster 1 in this ranking. Latvia, Thailand, and Cyprus, which have the highest rankings in Cluster 1, can be used as benchmark countries for Iran.

 National competitiveness has become the main focus of policymakers and governments. Therefore, in this study, a framework based on national competitiveness index was presented to determine the national competitiveness priorities as well as benchmark countries for Iran. In this framework, the countries of the world were first clustered based on competitive pillars using fuzzy c-means algorithm. Competitive columns were then weighed in each cluster using CCSD method. Finally, the countries of each cluster were ranked using the WASPAS method. Based on the proposed framework, competitive factors and priorities as well as benchmark countries for the Iran were determined.

The North Atlantic region is always exposed to severe storms, which are considered one of the most severe natural hazards in the climate field, and every year they cause serious damage to the economic infrastructure and human casualties in the areas affected by this event. These storms can help in the analysis and crisis management plans of this hazard and land preparation. The development of data collection and data mining technologies enables a more detailed study of this phenomenon. This issue requires the use of simple and efficient methods to investigate the behavior and extract the pattern from the database of this phenomenon. In this research, using spatial statistics methods, the trend of changes in the movement of tropical storms in the North Atlantic Ocean and the identification of their governing patterns in the period of 1995-2015 have been analyzed. The obtained results confirm the cluster pattern governing this phenomenon and that the occurrence of storms are not random events and follow spatial and temporal patterns in the studied area. The pattern of storms has a cluster pattern with the maximum value of the average value of the nearest neighborhood of 0.74 and the minimum value of 0.47. Also, the value of the general Moran index, the highest and the lowest correlation and clustering were calculated in 2006 with an index number of 0.66 and 2009 with an index number of 0.12 respectively, and a map of clusters and non-clusters and hot spots was prepared. With a better understanding of the patterns governing the movement of storms, it is possible to reduce possible damages caused by storms. Based on this, as a suggestion for future research, it is possible to include the effect of other parameters such as temperature, water salinity, and atmospheric general circulation systems, which play a significant role in the distribution of the distribution of the occurrence of storms, in the modeling and data mining of storms and get results closer to reality. Finally, as a useful research, the results of clusters and hot spots can be used in predicting the movement of storms in the future.

Transit oriented development (TOD) is a strategy for integrating land use and public transportation systems in urban areas around metro and BRT stations. TOD means maximum density, maximum diversity, pedestrian facilities, public spaces around stations, and design of stations as leisure centers within a 10-minute walk of transportation stations. This research was carried out with the aim of clustering 9 stations on line 1 of Mashhad metro, by developing a node-place model in TOD areas and using Ward cluster analysis. The present research is applied-evaluation in terms of purpose and descriptive-analytical and quantitative in terms of nature and method. Data analysis was also performed using Execle and Arc map software. In this study, municipal data were used; And land use information was prepared in the field and entered into Arc map software. To achieve this goal, a circle with a radius of 500 meters was drawn to the center of each station and three indicators of place, node and access were evaluated. In the next step, the scatter diagram of the node-place model was drawn. The stations were clustered by adding access criteria (including IPCA and bicycle route indicators). The results of the node-place diagram show that the average place criterion is higher than the node. The average location is 0.303 and the average node is 0.274. Also, all but one station is in a balanced area. Ward clustering results show; By adding access criteria, the position of the stations in the clusters changes and they are classified into four general categories. Also, the degree of correlation between the two criteria of place and node with positive access. But this number is very low for access and node and r= 0.12 and for access and place r= 0.6. In addition, it can be said that the standard of the node decreases from the city center to the surrounding area. The place criterion is affected by the main intersections and is more in the old neighborhoods of commercial units and population. Accessibility in new neighborhoods has increased due to more intersections and smaller urban blocks.

Despite the many capabilities of spatial statistics in modeling and explaining various environmental features and identifying the patterns governing their distribution, the previous studies have considerably neglected these capabilities, especially in the field of water studies. The present study aims to model the patterns governing the distribution of water wells in the Mahidasht watershed in Kermanshah province using spatial statistics. The bases used to identify these patterns are the "Locations" and "Attributes" of the wells, which are performed using five common techniques in spatial statistics, including: Average Nearest neighbor, Getis-Ord General G, Spatial Autocorrelation (Moran I), Cluster and outlier analysis, and Hot spot analysis. Required data were obtained from the Kermanshah Regional Water Company. The results of the present study showed that the pattern governing the dispersion of agricultural wells and the pattern governing the dispersion of high values (high depth of wells) in the Mahidasht watershed do not confirm the null hypothesis and follow the cluster pattern. The general pattern of dispersion of wells in the study area since 1950s to 2000s has changed from a random pattern to the cluster one. Cluster and outlier analyses as well as hotspot analysis indicated that the northern and northeastern parts of the watershed are critical in terms of uncontrolled abstraction of groundwater resources.

Climate change is the greatest price society is paying for decades of environmental neglect. The impact of global warming is most visible in the rising threat of climate-related natural disasters. Globally, meteorological disasters more than doubled, from an average of forty-five events a year to almost 120 events a year (Vinod, 2017). Climate change refers to changes in the distributional properties of climate characteristics like temperature and precipitation that persist across decades (Field et al., 2014). Because precipitation is related to temperature, scientists often focus on changes in global temperature as an indicator of climate change. Valipour et al. (2021) reported the mean of monthly the global mean surface temperature (GMST) anomalies in 2000–2019 is 0.54 C higher than that in 1961–1990. Many studies have been done on climate change in Iran. These studies have mostly studied the mean and extreme temperature trends (Alijani et al., 2011; Masoudian and Darand, 2012). In general, the results of previous studies showed that the statistics of mean, maximum and minimum air temperature in most parts of the Iranian plateau have increased in recent decades. Also, the increase of minimum temperature is greater than maximum temperature. A review of the research background shows that we need to understand more about regional climate change in Iran. Therefore, present study performs the climate change of 14 extreme temperature indices using multivariate statistical methods at the regional scale.

Historical climate observations including daily maximum and minimum temperature were obtained from the Iranian Meteorology Organization for the period 1968 to 2017 at 39 stations. In this paper, 14 extreme temperature indices defined by ETCCDI were analyzed. The indices are as follows: (1) Annual maxima of daily maximum temperature (TXx); (2) Annual maxima of daily minimum temperature (TNx); (3) Annual minima of daily maximum temperature (TXn); (4) Annual minima of daily minimum temperature (TNn); (5) Cold nights (TN10p); (6) Cold days (TX10p); (7) Warm night (TN90p); (8) Warm day (TX90p); (9) Frost days (FD); (10) Icing days (ID); (11) Summer day (SU); (12) Tropical nights (TR); (13) The warm spell duration index (WSDI) and (14) the cold spell duration index (CSDI). The extreme temperature indices were extracted using R software environment, RclimDex extension. The Mann–Kendall Test and Sen’s Slope Method was employed to assess the trends in 14 extreme temperature indices. To identify homogeneous groups of stations with similar annual thermal regimes, Principal Component analysis (PCA) and Clustering (CL) was applied. Pearson correlation coefficient was used to investigate the relationship between height and trend slope.

All the extreme temperature intensity indices (TXx, TNx, TXn, and TNn) showed increasing trends during 1968 to 2017. The increasing trends of TXx, TNx, TXn, and TNn were 0.2, 0.3, 0.44, and 0.5 ° C per decade, respectively. These results indicated that the extreme warm events increased and the extreme cold events decreased. The average of the extreme temperature frequency indices over Iran showed that the frequency of warm night (TN90p) and warm day (TX90p) significantly increased with a rate of 6.9 and 4.2 day per decade, respectively. Also, the frequency of cold night (TN10p) and cold day (TX10p) significantly fell with a decrease rate of 3.8 and 3.8 day per decade, respectively. The frequency of warm nights (TN90p) was higher than that of warm days (TX90p). The result indicated that the trend of nighttime extremes were stronger than those for daytime extremes. The average of frost days (FD) and icing days (ID) indices over Iran showed decreasing trends during 1968 to 2017 with rates of 3 and 1.1 d per decade, respectively. While, the averaged of summer days (SU) and tropical days (TR) indices over Iran showed increasing trends with rates of 4.4 and 6.4 day per decade, respectively. The warm spell duration index (WSDI) indices showed a clear increase, with a rate of 2.1 per decade. In contrast the cold spell duration index (CSDI) showed a significant decrease, with a rate of 1.7 per decade. In general, the cold indices displayed decreasing trends, whereas the warm indices displayed increasing trends over most of Iran. Pearson correlation coefficient between height and Sen’s Slope was estimated to be equal to -0.62 (p < 0.01). In general, the results of this study showed that there is a negative correlation between the elevation factor and the Sen’s Slope of warm extreme indices. That is, as the altitude decreases, the Sen’s Slope increases. Therefore, the stations located in low altitude have experienced stronger increasing trends than in high altitude. The area of ​​Iran was classified into four clusters using PCA and CL methods. Cluster 1 has experienced the strongest increasing trends. The average height of cluster 1 is 535 meters. Approximately 38% of the studied stations were located in cluster 1. Cluster 2 showed a moderate heating trends. 33% of the stations were located in cluster 2. Most of the stations of cluster 2 are located in the northwest and west of Iran. Cluster 3 showed a weak increasing trends compared to clusters 1 and 2. The stations of cluster 3 did not show a special geographical concentration and were scattered in all parts of Iran. 18% of the studied stations are located in cluster 3. The stations of Cluster 4, have experienced weak decreasing trends, which was different from the other three clusters

In this study we analyzed the climate change of extreme temperature indices in Iran. The result showed that the frequency of warm nights, warm days, summer days and tropical days increased. Also, the frequency of cold nights, cold days, Frost days and icing days decreased. The warm spell duration index showed a clear increase. In contrast the cold spell duration index showed a significant decrease. In general, the extreme warm events increased and the extreme cold events decreased over most of Iran. There is a negative correlation between the elevation factor and the Sen’s Slope of extreme warm indices (R = -0.62). Therefore, the stations located in low altitude have experienced stronger increasing trends than in high altitude. The area of ​​Iran was classified into four clusters using PCA and CL methods. Cluster 1 has experienced the strongest increasing trends. The average height of cluster 1 is 535 meters. Therefore, the most heating have occurred in Low-lying areas of Iran. Cluster 2 and Cluster 3 showed a moderate and weak heating trends, respectively. The stations of Cluster 4, have not experienced clear trends.